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Montri Karnjanadechamontri@coe.psu.ac.thhttp://fivedots.coe.psu.ac.th/~montri

240-650Principles of Pattern

Recognition

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Chapter 1

Introduction

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Outline

• Pattern Recognition System• The Design Cycle• Learning and Adaptation

• Read Chapter 1 (Duda, Hart, and Stork)

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Motivations

• Pattern recognition has many very valuable civil as well as military applications– Automated target recognition– Automated Processing Systems– New Human Computer Interface– Biometrics

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Handwritten Address Interpretation System

• HWAI - http://www.cedar.buffalo.edu/HWAI/– The HWAI (Handwritten Address Interpretation) System

was developed at Center of Excellence for Document Analysis and Recognition (CEDAR) at University at Buffalo, The State University of New York. It resulted from many years of research at CEDAR on the problems of Address Block location, Handwritten Digit/Character/Word Recognition, Database Compression, Information Retrieval, Real-Time Image Processing, and Loosely-Coupled Multiprocessing.

– The following presentation is based on the demonstration pages at HWAI

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Handwritten Address Interpretation System – cont.

• Step 1: Digitization

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Handwritten Address Interpretation System – Cont.

• Step 2: Address Block Location

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Handwritten Address Interpretation System – Cont.

• Step 3: Address Extraction

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Handwritten Address Interpretation System – Cont.

• Step 4: Binarization

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Handwritten Address Interpretation System – Cont.

• Step 5: Line Separation

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Handwritten Address Interpretation System – Cont.

• Step 6: Address Parsing

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Handwritten Address Interpretation System – Cont.

• Step 7: Recognition– (a) State Abbreviation Recognition

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Handwritten Address Interpretation System – Cont.

• Step 7: Recognition– (b) ZIP Code Recognition

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Handwritten Address Interpretation System – Cont.

• Step 7: Recognition– (c) Street Number Recognition

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Handwritten Address Interpretation System – Cont.

• Step 8: Street Name Recognition

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Handwritten Address Interpretation System – Cont.

• Step 9: Delivery Point Codes

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Handwritten Address Interpretation System – Cont.

• Step 10: Bar coding

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IBM Voice Systems

• Voice enabling e-bussinesshttp://www-4.ibm.com/software/speech/enterprise/dcenter/demo_0.html

– Get information through speech recognition software ViaVoice

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Machine Demonstrates Superhuman Speech Recognition Abilities

• Developed by Jim-Shih Liaw and Theodore W. Berger at University of Southern California

• The following is the claim– “University of Southern California

biomedical engineers have created the world's first machine system that can recognize spoken words better than humans can. A fundamental rethinking of a long-underperforming computer architecture led to their achievement”.

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Statistical Pattern Recognition

• In statistical pattern recognition, recognition is done by classifying the input (represented as a set of measurements) into predefined categories

• The core questions we want to address– What is the best we can do (statistically) when

a set of measurements is given for input?– Which measurements should be used if we can

choose a subset of all the measurements?

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A Simple Example

• Suppose that we are given two classes 1 and 2

– P(1) = 0.7

– P(2) = 0.3

– No measurement is given• Guessing

– What shall we do to recognize a given input?– What is the best we can do statistically? Why?

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An Introductory Example

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Terminology

• Features– Measurements available to the pattern

recognition system

• Models– Each class is represented by a description in

mathematical forms, called a model

• Preprocessing– Segmentation

• Isolate the object of interest from the background and other objects

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Terminology - cont.

• Feature extraction– Is the measuring process that produces the

measurements, or called features

• Training samples– Models for classes are often specified by

samples with known labels. These samples are called training samples

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Terminology - cont.

• Cost/risk– The cost of a decision associated with the

recognition result

• Decision theory– The theory on optimal decision rules

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Terminology - cont.• Decision boundary

– Boundaries in the feature space of regions with different classes (decisions)

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Terminology - cont.

• Generalization– While classes can be specified by training

samples with known labels, the goal of a recognition system is to recognize novel inputs

– When a recognition system is over-fitted to training samples, it may give bad performance for typical inputs

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Terminology - cont.

• Generalization - continued

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Terminology - cont.

• Generalization - continued

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Terminology - cont.

• Generalization - continued

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Terminology - cont.- Analysis by synthesis model

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Designing a Pattern Recognition System

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Designing a Pattern Recognition System

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Steps in a Pattern Recognition System

• Sensing– Measuring of features, such as a digital

camera, or a microphone– We assume the measurements are given

• Segmentation and grouping– In the fish example, we have to isolate a

fish from other fishes, other non-fish objects, or the background

– Segmentation/grouping is a very difficult problem

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Steps in a Pattern Recognition System – cont.

• Image segmentation is one of the most difficult problems in computer vision– Face detection, for example, can be viewed

as a image segmentation problem

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Steps in a Pattern Recognition System – cont

• In speech recognition, the segmentation problem is called source separation– Mixed speech signal– Separated signal source 1– Separated signal source 2

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Steps in a Pattern Recognition System – cont.

• Feature extraction/selection– A critical step for pattern recognition– Seeking distinguishing features that are

invariant to irrelevant transformations of the input

– Biometrics can be viewed as a feature selection problem

• Classification• Post-processing

– Context information– Multiple classifiers

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The Design Cycle

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Learning• Supervised learning

– A category label is given for each pattern in a training set

• Unsupervised learning– The system forms clusters or natural groupings

of the input patterns– The study of category formation

• Reinforcement learning– No desired output is provided; the feedback is

given